本論文提出一個新穎的刺繡織物的自動化影像分析系統，此系統能對刺繡布影像進行自動化分色及紋理分析。首先以掃描器取得刺繡織物在紅綠藍(RGB)格式的數位影像，再將RGB格式的數位影像轉換以L表示亮度分量、A表示綠色和紫紅色分量與B表示藍色和黃色分量的CIELAB色彩空間。對CIELAB色彩空間中亮度層做抑制雜訊的影像處理實驗，如:均值濾波、中值濾波、一次與兩次小波轉換法處理，並藉由峰値信號雜訊比(Peak Signal to Noise Ratio)指標來比較原始影像和經抑制雜訊處理後的影像品質。結果顯示一次小波轉換法在抑制雜訊處理中除可提高數位影像的平滑化及具較少的影像處理失真效果，所以使用一次小波轉換進行刺繡織物影像的濾波處理
。接著，再以模糊C平均(Fuzzy C-means)聚類演算法於刺繡布的影像上進行區域分裂的分色。刺繡織物的主紋理分析是使用熵和Hough變換方法獲得到主紋理特徵，而紗線紋理分析，以賈柏濾波器處理產生紗線紋理的特徵，使用一組具有不同頻率和方向的賈伯濾波器由刺繡織物濾波處理後的影像中提取出紗線紋理訊息，賈伯濾波器中使用的方向參數是由紋理方向計算演算法中得到，再以3個尺度和3個方向的賈柏濾波器組分別對待測的刺繡織物影像進行迴旋積運算提取灰階的紗線紋理訊息。紗線紋理特徵的顯現是透過自動化二值化的處理，通過Otsu演算法，它可以計算出最佳化的門檻值，並且執行二值化處理。最後經過細線化方法處理、去除孤立點而擷取紗線紋理特徵結果。由實驗結果，可看出本論文的方法適用於刺繡布的分色和紋理特徵分析。

This study created an automated color and texture image analyzing system for embroidery fabrics. First, a scanner was used to obtain the digitized color image of the embroidery fabric in RGB mode. Then the image was converted to a digitized color image in CIELAB mode. In this stage, noise reduction methods such as mean filtering, median filtering, one-time and two-time wavelet transformation processing on lightness layer of CIELAB color space were conducted. The Peak Signal to Noise Ratio (PSNR) method compared the digitized image quality status between the original to the de-noising image. In the results, one-time wavelet transformation de-noising processing improved the digitized image smoothing with low processing loss. The pre-processed embroidery fabric images were developed with the one-time wavelet transformation. The Fuzzy C-means (FCM) clustering method was employed to run color separation and regional separation. The main texture analysis of embroidery fabrics is based in Entropy and Hough transform approach to main texture feature is obtained. The yarn texture analysis was based on Gabor filters approach to obtain yarn texture features. Pre-processed embroidery fabric images used a set of Gabor filters with different frequencies and orientations for extracted yarn texture information. The orientation parameter for Gabor filters was one algorithm of texture recognition on directional fields. Using three scales and three directional convolution operations, the Gabor filters detected gray level yarn texture information of the embroidery fabric. The appearance of the yarn texture feature was found with automated threshold processing. It calculated the optimal threshold through the Otsu algorithm and executed binary processing. Finally, the result was
 
achieved by thinning method processing, which removed the outlier to obtain yarn texture feature. The results of this study confirmed that the method could be applied to embroidery color and texture analysis.

摘要 II
ABSTRACT III
誌謝 V
目錄 VI
圖目錄 IX
表目錄 XII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧探討 2
1.3 論文架構 5
1.4 研究流程 6
第二章 刺繡的技術 7
2.1 刺繡的起源 7
2.2 電腦刺繡的興起與發展 8
2.3 電腦刺繡的針法 11
2.4 電腦刺繡的流程 13
第三章 數位影像處理技術 14
3.1 色彩空間原理 14
3.1.1 RGB色彩空間 15
3.1.2 CIELAB色彩空間 16
3.1.3 RGB轉CIELAB色彩空間 17
3.1.4 CIELAB 轉RGB 色彩空間 18
 
3.2 影像濾波處理 19
3.2.1 均值濾波 19
3.2.2 中值濾波 20
3.2.3 小波轉換 20
3.3 峰値信號雜訊比 23
第四章 刺繡織物影像的色彩分析 24
4.1 模糊C平均聚類演算法 24
4.2 二元區域分裂法 27
第五章 刺繡織物紗線的紋理分析 29
5.1 熵 29
5.2 HOUGH轉換法 30
5.3 紋理方向計算演算法 31
5.4 賈伯轉換法 33
5.5 OTSU演算法 39
5.6 細線化演算法 41
第六章 實驗結果與討論 44
6.1 實驗設備 44
6.2 實驗流程 45
6.3 RGB影像轉換成CIELAB色彩空間灰階影像 47
6.4 PSNR法應用於影像經濾波處理後的影像品質評估 48
6.5 聚類演算法和二元區域分裂法應用於刺繡織物的分色 53
6.6 HOUGH轉換法應用於刺繡織物的主紋理特徵 57
6.7 紋理方向計算演算法應用於紗線紋理訊息的方向場 64
6.8 賈伯濾波器應用於刺繡織物的紗線紋理特徵 66
 
6.9 自動二值化與細線化應用於擷取紗線的紋理特徵 74
第七章 結論與未來研究方向 80
7.1 結論 80
7.2 未來研究方向 82
參考文獻 83

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